import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)


def add_layer(inputs, in_size, out_size, activation_function=None):
    Weights = tf.Variable(tf.random_normal([in_size, out_size]))
    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)
    Wx_plus_b = tf.matmul(inputs, Weights) + biases
    Wx_plus_b = tf.nn.dropout(Wx_plus_b,keep_prob)
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b)
    return outputs


def comput_accuracy(v_xs, v_ys):
    global prediction
    y_pre = sess.run(prediction, feed_dict={xs:v_xs,keep_prob:1})
    correct_prediction = tf.equal(tf.argmax(y_pre, 1), tf.argmax(v_ys, 1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    result = sess.run(accuracy, feed_dict={xs:v_xs, ys:v_ys,keep_prob:1})
    return result


def weight_variable(shape):
    inital = tf.truncated_normal(shape, stddev=0.1)
    return tf.Variable(inital)


def bias_variable(shape):
    inital = tf.constant(0.1, shape=shape)
    return tf.Variable(inital)


def conv2d(x, W):
    # stride[1,x_movement,y_movement,1]
    # Must have stride[0] and stride[3] is 1
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')


def max_pool_2x2(x):
    # stride[1,x_movement,y_movement,1]
    # Must have stride[0] and stride[3] is 1
    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')


xs = tf.placeholder(tf.float32, [None, 784])
ys = tf.placeholder(tf.float32, [None, 10])
keep_prob = tf.placeholder(tf.float32)

x_image = tf.reshape(xs,[-1,28,28,1])
print(x_image.shape)  #[n_samples,28,28,1]

## conv1 layer ##
W_conv1 = weight_variable([5,5,1,32])  #patch  5x5  ,in size 1 , out size 32
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1) + b_conv1) #output size 28x28x32
h_pool1 = max_pool_2x2(h_conv1)                         #output size 14x14x32

## conv2 layer ##
W_conv2 = weight_variable([5,5,32,64])  #patch  5x5  ,in size 32 , out size 64
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1,W_conv2) + b_conv2) #output size 14x14x64
h_pool2 = max_pool_2x2(h_conv2)                         #output size 7x7x64

## func1 layer ##
W_fc1 = weight_variable([7*7*64,1024])
b_fc1 = bias_variable([1024])
h_pool2_flat = tf.reshape(h_pool2,[-1,7*7*64])      #[n_saples,7,7,64] ->> [n_samples,7x7x64]
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat,W_fc1)+ b_fc1)
h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob)

## func2 layer ## 
W_fc2 = weight_variable([1024,10])
b_fc2 = bias_variable([10])
prediction = tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2)+ b_fc2)

#loss
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction), reduction_indices=[1]))

train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

init = tf.initialize_all_variables()
sess = tf.Session()
sess.run(init)

for i in range(1000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={xs:batch_xs, ys:batch_ys, keep_prob:0.6})
    if i % 50 == 0:
        print(comput_accuracy(batch_xs, batch_ys))
            
        
